A comprehensive review on ensemble deep learning: Opportunities and challenges
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In machine learning, two approaches outperform traditional algorithms: ensemble learning and deep learning. The former refers to methods that integrate multiple base models in the same framework to obtain a stronger model that outperforms them. The success of an ensemble method depends on several factors, including how the baseline models are trained and how they are combined. In the literature, there are common approaches to building an ensemble model successfully applied in several domains. On the other hand, deep learning-based models have improved the predictive accuracy of machine learning across a wide range of domains. Despite the diversity of deep learning architectures and their ability to deal with complex problems and the ability to extract features automatically, the main challenge in deep learning is that it requires a lot of expertise and experience to tune the optimal hyper-parameters, which makes it a tedious and time-consuming task. Numerous recent research efforts have been made to approach ensemble learning to deep learning to overcome this challenge. Most of these efforts focus on simple ensemble methods that have some limitations. Hence, this review paper provides comprehensive reviews of the various strategies for ensemble learning, especially in the case of deep learning. Also, it explains in detail the various features or factors that influence the success of ensemble methods. In addition, it presents and accurately categorized several research efforts that used ensemble learning in a wide range of domains.
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Ensemble strategies improve classification performances in imbalanced tasks.
“ES demonstrated to improve classification performances in a wide range of tasks from various domains (Mohammed & Kora, 2023), and in many cases they perform better than more traditional methods when dealing with imbalanced classification tasks.”
From CiteFusion: an ensemble framework for citation intent classification harnessing dual-model binary couples and SHAP analyses (2025) · cites this paper as Mohammed & Kora, 2023
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Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses
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Paolini, L., Vahdati, S., Di Iorio, A., Wardenga, R., Heibi, I., & Peroni, S. (2025) · Scientometrics · doi:10.1007/s11192-025-05418-8 · Cited by 8 · see citing works
“An interesting research direction in classification tasks is related to ensemble strategies (ES), in which a set of weak - or baseline - learners are employed by means of an aggregation function to produce a single output through the different predictions of the base models (Mohammed & Kora, 2023).”
- Authors
- Mohammed, A., & Kora, R.
- Affiliations
- Cairo University (EG)
- Corresponding author
- Mohammed, Ammar
- Kora, Rania
- Venue
- Journal of King Saud University - Computer and Information Sciences
- Published
- 2023-02
- Volume
- 35
- Issue
- 2
- Pages
- 757-774
- ISSN
- 1319-1578 (ISSN-L)
- OpenAlex
- W4318677156
- Cited by
- 1,127
- References
- 203
- Citations by year
- 2026: 238 · 2025: 443 · 2024: 357 · 2023: 88 · 2022: 1
- FWCI
- 105.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of King Saud University - Computer and Information Sciences (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- ensemble deep learning, ensemble learning, deep learning, hyperparameter optimization, deep learning architectures
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